extratrees
API
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extratrees/lamp.extratrees

lamp.extratrees

package lamp.extratrees

Members list

Type members

Classlikes

case class ClassificationLeaf(targetDistribution: Seq[Double]) extends ClassificationTree

Attributes

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object ClassificationLeaf

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case class ClassificationNonLeaf(left: ClassificationTree, right: ClassificationTree, splitFeature: Int, cutpoint: Double, splitMissingIsLess: Boolean) extends ClassificationTree

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object ClassificationNonLeaf

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sealed trait ClassificationTree

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class Object
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Known subtypes
object ClassificationTree

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case class RegressionLeaf(targetMean: Double) extends RegressionTree

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object RegressionLeaf

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case class RegressionNonLeaf(left: RegressionTree, right: RegressionTree, splitFeature: Int, cutpoint: Double, splitMissingIsLess: Boolean) extends RegressionTree

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object RegressionNonLeaf

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sealed trait RegressionTree

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class Object
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Known subtypes
object RegressionTree

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Value members

Concrete methods

def buildForestClassification(data: Mat[Double], target: Vec[Int], sampleWeights: Option[Vec[Double]], numClasses: Int, nMin: Int, k: Int, m: Int, parallelism: Int, bestSplit: Boolean, maxDepth: Int, seed: Long): Seq[ClassificationTree]

Train an extratrees classifier forest

Train an extratrees classifier forest

Value parameters

bestSplit

if true then the split is not random but the best among possible splits.

k

number of features to consider in each split step. The best among these will be chosen.

m

number of trees

maxDepth

maximum tree depth

nMin

minimum sample size for splitting a node

seed

Returns a list of ClassificationTree objects which can be passed to predictClassification

Attributes

def buildForestRegression(data: Mat[Double], target: Vec[Double], nMin: Int, k: Int, m: Int, parallelism: Int, bestSplit: Boolean, maxDepth: Int, seed: Long): Seq[RegressionTree]

Train an extratrees regression forest

Train an extratrees regression forest

Value parameters

bestSplit

if true then the split is not random but the best among possible splits.

k

number of features to consider in each split step. The best among these will be chosen.

m

number of trees

maxDepth

maximum tree depth

nMin

minimum sample size for splitting a node

seed

Returns a list of RegressionTree objects which can be passed to predictRegression

Attributes

def predictClassification(trees: Seq[ClassificationTree], samples: Mat[Double]): Mat[Double]

Prediction from a set of trees

Prediction from a set of trees

Returns a matrix of nxm where n is the number of samples m is the number of classes, column c corresponds to class c.

Attributes

def predictRegression(trees: Seq[RegressionTree], samples: Mat[Double]): Vec[Double]
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